Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. IBM says that deep learning attempts to mimic the human brain—albeit far from matching its ability—enabling systems to cluster data and make predictions with incredible accuracy.
Deep learning drives much artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. In our latest article, we talk about the many benefits and applications of deep learning across segments.
Even though many companies and research institutions seem to have their hands on every possible area within deep learning, a clear pattern is emerging.
In this article by Analytics India, they explore how big tech organizations like DeepMind, Open AI, Facebook, Google, IBM, Microsoft, Apple & Amazon are heading in terms of their research efforts in deep learning.
The tech giants are truly delving deep when it comes to deep learning!
The existential threat of Covid-19 has highlighted an acute need to develop working therapeutics against emerging health concerns. One of the luxuries deep learning has afforded us is the ability to modify the landscape as it unfolds — so long as we can keep up with the viral threat, and access the right data.
As with all new medical maladies, oftentimes the data need time to catch up, and the virus takes no time to slow down, posing a difficult challenge as it can quickly mutate and become resistant to existing drugs. This led scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Jameel Clinic for Machine Learning in Health to ask: How can we identify the right synergistic drug combinations for the rapidly spreading SARS-CoV-2?
This MIT News article leads us into a path-breaking discovery!
AI winters were not due to imagination traps, but due to lack of imagination. Imaginations bring order out of chaos. Deep learning with deep imagination is the road map to AI springs and AI autumns - Amit Ray
Beckham Institute Biophotonics Imaging Laboratory researchers have applied deep learning to polarization-sensitive optical coherence tomography (PS-OCT) to improve cancer diagnostic tools.
Deep learning has allowed the research team to create software that can work with OCT systems to deliver polarizations sensitivity. Deep learning enabled a more advanced method of picking up subtle features in images, which can be used for more accurate segmentation and classification.
There is a significant role that deep learning will play in the advancement of medical science!
Researchers at Heidelberg University and the University of Bern have recently devised a technique to achieve fast and energy-efficient computing using spiking neuromorphic substrates. This strategy, introduced in a paper published in Nature Machine Intelligence, is a rigorous adaptation of a time-to-first-spike (TTFS) coding scheme, together with a corresponding learning rule implemented on certain networks of artificial neurons.
In their future work, the researchers would also like to extend their framework so that it can process spatiotemporal data. To do this, they would need to also train it on time-varying data, such as audio or video recordings.
From big tech to big computers, deep learning is bound to have an impact across segments!
In the end...
Real-world deep learning applications are a part of our daily lives, but in most cases, they are so well-integrated into products and services that users are unaware of the complex data processing that is taking place in the background. With technology rapidly evolving, deep learning is bound to become deeply ingrained across various facets of our lives.
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